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A survey on feature selection approaches for clustering
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2020-01-02 , DOI: 10.1007/s10462-019-09800-w
Emrah Hancer , Bing Xue , Mengjie Zhang

The massive growth of data in recent years has led challenges in data mining and machine learning tasks. One of the major challenges is the selection of relevant features from the original set of available features that maximally improves the learning performance over that of the original feature set. This issue attracts researchers’ attention resulting in a variety of successful feature selection approaches in the literature. Although there exist several surveys on unsupervised learning (e.g., clustering), lots of works concerning unsupervised feature selection are missing in these surveys (e.g., evolutionary computation based feature selection for clustering) for identifying the strengths and weakness of those approaches. In this paper, we introduce a comprehensive survey on feature selection approaches for clustering by reflecting the advantages/disadvantages of current approaches from different perspectives and identifying promising trends for future research.

中文翻译:

聚类特征选择方法综述

近年来数据的大量增长给数据挖掘和机器学习任务带来了挑战。主要挑战之一是从原始可用特征集中选择相关特征,最大限度地提高原始特征集的学习性能。这个问题引起了研究人员的注意,导致文献中出现了各种成功的特征选择方法。尽管存在一些关于无监督学习(例如聚类)的调查,但在这些调查(例如,基于进化计算的聚类特征选择)中缺少许多关于无监督特征选择的工作,以识别这些方法的优缺点。在本文中,
更新日期:2020-01-02
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